This will be follow-up from the Sang lab meeting on 05/21/2020.

Summary

Changes to Figure 3

  1. Figure 3a

     i) pull the granulocyte progenitor population out of granulocytes
 ii) split up macrophages and monocytes
 iii) prepare image (or supplemental image) with the singleR naming of the cells

  2. Figure 3c

     i) changing the heatmap to a bar plot

  3. Figure 3d

     i) look at different color schemes to make the contrast more stark

Additional Work

  1. DEG Work

     i) provide MK DEG list
 ii) provide violin plots for all MK DEGs
 iii) change the color of the violin plots (blue = Migr, red = Mpl)
 iv) run DE analysis on other clusters

Changes to Figure 3

Adding in additional clusters to Figure 1 and 2

Including the granulocyte progenitor population, and splitting up the monocyte and macrophage populations.

##  Granulocyte1  Granulocyte2  Granulocyte3        B cell            MK 
## "Granulocyte" "Granulocyte" "Granulocyte"      "B-cell"          "MK" 
##  Granulocyte4     Stem Cell      Monocyte    Macrophage     Erythroid 
## "Granulocyte"        "HSPC"    "Monocyte"  "Macrophage"   "Erythroid" 
##    B-cell pro     T-cell/NK           MEP 
##      "B-cell"   "T-cell/NK"         "MEP"
## [1] "Granulocyte" "B-cell"      "MK"          "HSPC"        "Monocyte"   
## [6] "Macrophage"  "Erythroid"   "T-cell/NK"   "MEP"

New 3b

Adjusting Figure 3c for the new clusters

3c Alternatives

Looking at creating bar plots for the figures instead of heatmap

Another version of a potential bar chart

##     Cell Type Condition Count Percentage Condition2
## 1 Granulocyte   Control   895         34    Control
## 2      B-cell   Control   946         36    Control
## 3          MK   Control    21          1    Control
## 4        HSPC   Control   127          5    Control
## 5    Monocyte   Control   192          7    Control
## 6  Macrophage   Control   156          6    Control
## [1] "MEP"         "T-cell/NK"   "Erythroid"   "Macrophage"  "Monocyte"   
## [6] "HSPC"        "MK"          "B-cell"      "Granulocyte"

Figure 3d

Adding labels to the heatmap

## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.

SingleR supplementary figure

Creating a heatmap to see how SingleR labeled the individual cells in each cluster

Figure 3c Plots

Visualizing the data with a bar plots instead of heat plots

MK DE Genes

Here are all the violin plots for all the differentially expressed genes in the MK cluster.

##    Gene  P.value Avg.Log.FC Control.Pct Mipl.Pct Pct..Diff Adj..P.value
## 1   Mpo 6.04e-19       1.73       0.476    0.298      0.18     1.87e-14
## 2 Csrp3 2.42e-29       1.63       0.905    0.290      0.62     7.53e-25
## 3 Nedd4 1.17e-27       1.43       1.000    0.775      0.23     3.64e-23
## 4 Hmgb2 1.19e-13       1.41       1.000    0.923      0.08     3.69e-09
## 5 Stmn1 8.60e-08       1.39       0.524    0.273      0.25     2.67e-03
## 6 Ms4a3 2.77e-14       1.34       0.476    0.138      0.34     8.59e-10
## [1] 123
## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##       
## This message will be shown once per session.

Profibrotic Factors

Taking a look at the list of profibrotic factors provided by Priya.

##    Mode   FALSE    TRUE 
## logical      18      43
## [1] "Profibrotic factors that were not found in our analysis"
##  [1] "Il1"       "Il8"       "Il12"      "Tnfa"      "Gmcsf"     "Gcsf"     
##  [7] "Pdgf"      "Tsp1"      "Tsp"       "Coliv"     "Col4"      "Fn"       
## [13] "Cxcl4"     "Cscl7"     "Fgf"       "Vegf"      "Tsp1"      "C20orf194"
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Pdgfd.

## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Ctgf.
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Col3a1.

## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Mmp3.

## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Timp4.

## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Bmp6.